Overview

Dataset statistics

Number of variables25
Number of observations35951
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 MiB
Average record size in memory200.0 B

Variable types

Numeric21
Categorical4

Alerts

0.1 has constant value "0"Constant
2 is highly overall correlated with 2.1High correlation
1 is highly overall correlated with 1.1High correlation
85 is highly overall correlated with 74.8 and 5 other fieldsHigh correlation
74.8 is highly overall correlated with 85 and 5 other fieldsHigh correlation
68 is highly overall correlated with 85 and 5 other fieldsHigh correlation
74 is highly overall correlated with 85 and 6 other fieldsHigh correlation
71.4 is highly overall correlated with 85 and 6 other fieldsHigh correlation
66 is highly overall correlated with 85 and 5 other fieldsHigh correlation
100 is highly overall correlated with 89.4High correlation
89.4 is highly overall correlated with 100 and 1 other fieldsHigh correlation
65 is highly overall correlated with 89.4High correlation
21 is highly overall correlated with 9.5High correlation
9.5 is highly overall correlated with 21 and 1 other fieldsHigh correlation
0.2 is highly overall correlated with 9.5High correlation
29.8 is highly overall correlated with 85 and 7 other fieldsHigh correlation
29.8.1 is highly overall correlated with 74 and 3 other fieldsHigh correlation
29.7 is highly overall correlated with 29.8 and 1 other fieldsHigh correlation
2.1 is highly overall correlated with 2High correlation
1.1 is highly overall correlated with 1High correlation
29.7 is highly skewed (γ1 = -36.29450683)Skewed
2 has 5112 (14.2%) zerosZeros
0 has 1497 (4.2%) zerosZeros
0.2 has 25943 (72.2%) zerosZeros
0.3 has 24983 (69.5%) zerosZeros

Reproduction

Analysis started2023-02-25 00:29:54.528920
Analysis finished2023-02-25 00:31:11.886364
Duration1 minute and 17.36 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9973575
Minimum0
Maximum6
Zeros5112
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:11.982151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9990453
Coefficient of variation (CV)0.66693589
Kurtosis-1.2492647
Mean2.9973575
Median Absolute Deviation (MAD)2
Skewness0.0041364735
Sum107758
Variance3.9961822
MonotonicityNot monotonic
2023-02-25T06:31:12.095733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 5184
14.4%
3 5160
14.4%
6 5136
14.3%
2 5135
14.3%
5 5112
14.2%
4 5112
14.2%
0 5112
14.2%
ValueCountFrequency (%)
0 5112
14.2%
1 5184
14.4%
2 5135
14.3%
3 5160
14.4%
4 5112
14.2%
5 5112
14.2%
6 5136
14.3%
ValueCountFrequency (%)
6 5136
14.3%
5 5112
14.2%
4 5112
14.2%
3 5160
14.4%
2 5135
14.3%
1 5184
14.4%
0 5112
14.2%

2.1
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.0 KiB
2
23298 
3
9215 
0
 
2305
1
 
1133

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35951
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 23298
64.8%
3 9215
 
25.6%
0 2305
 
6.4%
1 1133
 
3.2%

Length

2023-02-25T06:31:12.227488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-25T06:31:12.387795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 23298
64.8%
3 9215
 
25.6%
0 2305
 
6.4%
1 1133
 
3.2%

Most occurring characters

ValueCountFrequency (%)
2 23298
64.8%
3 9215
 
25.6%
0 2305
 
6.4%
1 1133
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35951
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 23298
64.8%
3 9215
 
25.6%
0 2305
 
6.4%
1 1133
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 35951
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 23298
64.8%
3 9215
 
25.6%
0 2305
 
6.4%
1 1133
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35951
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 23298
64.8%
3 9215
 
25.6%
0 2305
 
6.4%
1 1133
 
3.2%

0
Real number (ℝ)

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.50032
Minimum0
Maximum23
Zeros1497
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:12.517457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q317.5
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9221134
Coefficient of variation (CV)0.60190616
Kurtosis-1.2041606
Mean11.50032
Median Absolute Deviation (MAD)6
Skewness-1.1085084 × 10-5
Sum413448
Variance47.915654
MonotonicityNot monotonic
2023-02-25T06:31:12.648613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 1498
 
4.2%
2 1498
 
4.2%
23 1498
 
4.2%
22 1498
 
4.2%
21 1498
 
4.2%
20 1498
 
4.2%
19 1498
 
4.2%
18 1498
 
4.2%
17 1498
 
4.2%
16 1498
 
4.2%
Other values (14) 20971
58.3%
ValueCountFrequency (%)
0 1497
4.2%
1 1498
4.2%
2 1498
4.2%
3 1498
4.2%
4 1498
4.2%
5 1498
4.2%
6 1498
4.2%
7 1498
4.2%
8 1498
4.2%
9 1498
4.2%
ValueCountFrequency (%)
23 1498
4.2%
22 1498
4.2%
21 1498
4.2%
20 1498
4.2%
19 1498
4.2%
18 1498
4.2%
17 1498
4.2%
16 1498
4.2%
15 1498
4.2%
14 1498
4.2%

0.1
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.0 KiB
0
35951 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35951
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 35951
100.0%

Length

2023-02-25T06:31:12.779672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-25T06:31:12.913146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 35951
100.0%

Most occurring characters

ValueCountFrequency (%)
0 35951
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35951
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35951
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 35951
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35951
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35951
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35951
100.0%

6
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.68051
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:13.030423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7957986
Coefficient of variation (CV)0.56093831
Kurtosis-1.2026807
Mean15.68051
Median Absolute Deviation (MAD)8
Skewness0.019606938
Sum563730
Variance77.366073
MonotonicityNot monotonic
2023-02-25T06:31:13.174631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
6 1439
 
4.0%
7 1320
 
3.7%
22 1176
 
3.3%
28 1176
 
3.3%
13 1176
 
3.3%
26 1176
 
3.3%
25 1176
 
3.3%
24 1176
 
3.3%
23 1176
 
3.3%
27 1176
 
3.3%
Other values (21) 23784
66.2%
ValueCountFrequency (%)
1 1152
3.2%
2 1152
3.2%
3 1152
3.2%
4 1152
3.2%
5 1152
3.2%
6 1439
4.0%
7 1320
3.7%
8 1152
3.2%
9 1152
3.2%
10 1152
3.2%
ValueCountFrequency (%)
31 672
1.9%
30 1080
3.0%
29 1104
3.1%
28 1176
3.3%
27 1176
3.3%
26 1176
3.3%
25 1176
3.3%
24 1176
3.3%
23 1176
3.3%
22 1176
3.3%

1
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.504826
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:13.316022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4273096
Coefficient of variation (CV)0.5268872
Kurtosis-1.1862428
Mean6.504826
Median Absolute Deviation (MAD)3
Skewness0.001143235
Sum233855
Variance11.746451
MonotonicityNot monotonic
2023-02-25T06:31:13.436673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 3360
9.3%
3 3024
8.4%
5 3024
8.4%
7 3024
8.4%
1 3023
8.4%
8 3000
8.3%
10 3000
8.3%
12 3000
8.3%
4 2928
8.1%
9 2904
8.1%
Other values (2) 5664
15.8%
ValueCountFrequency (%)
1 3023
8.4%
2 2760
7.7%
3 3024
8.4%
4 2928
8.1%
5 3024
8.4%
6 3360
9.3%
7 3024
8.4%
8 3000
8.3%
9 2904
8.1%
10 3000
8.3%
ValueCountFrequency (%)
12 3000
8.3%
11 2904
8.1%
10 3000
8.3%
9 2904
8.1%
8 3000
8.3%
7 3024
8.4%
6 3360
9.3%
5 3024
8.4%
4 2928
8.1%
3 3024
8.4%

1.1
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.0 KiB
2
9312 
3
8928 
4
8904 
1
8807 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35951
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 9312
25.9%
3 8928
24.8%
4 8904
24.8%
1 8807
24.5%

Length

2023-02-25T06:31:13.576573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-25T06:31:13.722253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 9312
25.9%
3 8928
24.8%
4 8904
24.8%
1 8807
24.5%

Most occurring characters

ValueCountFrequency (%)
2 9312
25.9%
3 8928
24.8%
4 8904
24.8%
1 8807
24.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35951
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 9312
25.9%
3 8928
24.8%
4 8904
24.8%
1 8807
24.5%

Most occurring scripts

ValueCountFrequency (%)
Common 35951
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 9312
25.9%
3 8928
24.8%
4 8904
24.8%
1 8807
24.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35951
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 9312
25.9%
3 8928
24.8%
4 8904
24.8%
1 8807
24.5%

2016
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.0 KiB
2017
8760 
2018
8760 
2019
8760 
2016
5135 
2020
4536 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters143804
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2017 8760
24.4%
2018 8760
24.4%
2019 8760
24.4%
2016 5135
14.3%
2020 4536
12.6%

Length

2023-02-25T06:31:13.852931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-25T06:31:14.005951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 8760
24.4%
2018 8760
24.4%
2019 8760
24.4%
2016 5135
14.3%
2020 4536
12.6%

Most occurring characters

ValueCountFrequency (%)
2 40487
28.2%
0 40487
28.2%
1 31415
21.8%
7 8760
 
6.1%
8 8760
 
6.1%
9 8760
 
6.1%
6 5135
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 143804
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 40487
28.2%
0 40487
28.2%
1 31415
21.8%
7 8760
 
6.1%
8 8760
 
6.1%
9 8760
 
6.1%
6 5135
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 143804
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 40487
28.2%
0 40487
28.2%
1 31415
21.8%
7 8760
 
6.1%
8 8760
 
6.1%
9 8760
 
6.1%
6 5135
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 40487
28.2%
0 40487
28.2%
1 31415
21.8%
7 8760
 
6.1%
8 8760
 
6.1%
9 8760
 
6.1%
6 5135
 
3.6%

85
Real number (ℝ)

Distinct63
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.786265
Minimum34
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:14.171975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile56
Q173
median83
Q391
95-th percentile97
Maximum101
Range67
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.648272
Coefficient of variation (CV)0.15656464
Kurtosis0.080108224
Mean80.786265
Median Absolute Deviation (MAD)9
Skewness-0.79238734
Sum2904347
Variance159.97879
MonotonicityNot monotonic
2023-02-25T06:31:14.328773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 1536
 
4.3%
91 1368
 
3.8%
90 1344
 
3.7%
92 1344
 
3.7%
93 1320
 
3.7%
96 1272
 
3.5%
79 1248
 
3.5%
89 1248
 
3.5%
84 1128
 
3.1%
83 1128
 
3.1%
Other values (53) 23015
64.0%
ValueCountFrequency (%)
34 24
 
0.1%
38 48
 
0.1%
41 24
 
0.1%
42 48
 
0.1%
43 96
0.3%
44 48
 
0.1%
45 48
 
0.1%
46 48
 
0.1%
47 120
0.3%
48 48
 
0.1%
ValueCountFrequency (%)
101 48
 
0.1%
100 192
 
0.5%
99 168
 
0.5%
98 552
 
1.5%
97 840
2.3%
96 1272
3.5%
95 1536
4.3%
94 1104
3.1%
93 1320
3.7%
92 1344
3.7%

74.8
Real number (ℝ)

Distinct440
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.277138
Minimum27.6
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:14.497250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum27.6
5-th percentile48.1
Q162.9
median73.8
Q381.6
95-th percentile86.7
Maximum90
Range62.4
Interquartile range (IQR)18.7

Descriptive statistics

Standard deviation12.439201
Coefficient of variation (CV)0.17451881
Kurtosis-0.20757332
Mean71.277138
Median Absolute Deviation (MAD)8.7
Skewness-0.73360711
Sum2562484.4
Variance154.73373
MonotonicityNot monotonic
2023-02-25T06:31:14.655341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.3 336
 
0.9%
78.5 312
 
0.9%
67.3 288
 
0.8%
82 264
 
0.7%
81 264
 
0.7%
78.3 240
 
0.7%
84.7 240
 
0.7%
76.8 240
 
0.7%
84.9 240
 
0.7%
77.3 240
 
0.7%
Other values (430) 33287
92.6%
ValueCountFrequency (%)
27.6 24
0.1%
29.9 24
0.1%
30.3 24
0.1%
31.2 24
0.1%
31.7 24
0.1%
33.9 24
0.1%
34.6 48
0.1%
35.1 24
0.1%
37.3 24
0.1%
37.4 24
0.1%
ValueCountFrequency (%)
90 24
 
0.1%
89.8 24
 
0.1%
89.6 24
 
0.1%
89.4 24
 
0.1%
89 24
 
0.1%
88.8 48
0.1%
88.7 48
0.1%
88.5 24
 
0.1%
88.4 24
 
0.1%
88.3 96
0.3%

68
Real number (ℝ)

Distinct60
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.888376
Minimum19
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:14.848288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile38
Q152
median67
Q375
95-th percentile79
Maximum83
Range64
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.488967
Coefficient of variation (CV)0.21449063
Kurtosis-0.63186061
Mean62.888376
Median Absolute Deviation (MAD)9
Skewness-0.62888917
Sum2260900
Variance181.95224
MonotonicityNot monotonic
2023-02-25T06:31:15.035001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 2040
 
5.7%
75 2040
 
5.7%
73 1752
 
4.9%
76 1728
 
4.8%
74 1536
 
4.3%
72 1200
 
3.3%
68 1127
 
3.1%
69 984
 
2.7%
78 936
 
2.6%
60 864
 
2.4%
Other values (50) 21744
60.5%
ValueCountFrequency (%)
19 24
 
0.1%
21 24
 
0.1%
22 24
 
0.1%
25 72
0.2%
26 48
 
0.1%
28 48
 
0.1%
30 96
0.3%
31 144
0.4%
32 96
0.3%
33 120
0.3%
ValueCountFrequency (%)
83 48
 
0.1%
82 384
 
1.1%
81 432
 
1.2%
80 672
 
1.9%
79 792
 
2.2%
78 936
2.6%
77 2040
5.7%
76 1728
4.8%
75 2040
5.7%
74 1536
4.3%

74
Real number (ℝ)

Distinct62
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.260799
Minimum18
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:15.218282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile40
Q160
median70
Q376
95-th percentile79
Maximum83
Range65
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.345038
Coefficient of variation (CV)0.18630983
Kurtosis0.79343023
Mean66.260799
Median Absolute Deviation (MAD)7
Skewness-1.2164415
Sum2382142
Variance152.39997
MonotonicityNot monotonic
2023-02-25T06:31:15.384469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 3024
 
8.4%
78 2544
 
7.1%
76 2496
 
6.9%
75 2088
 
5.8%
70 1728
 
4.8%
73 1536
 
4.3%
74 1487
 
4.1%
79 1416
 
3.9%
66 1296
 
3.6%
68 1296
 
3.6%
Other values (52) 17040
47.4%
ValueCountFrequency (%)
18 24
 
0.1%
19 24
 
0.1%
20 24
 
0.1%
21 24
 
0.1%
23 24
 
0.1%
24 48
0.1%
27 24
 
0.1%
28 48
0.1%
29 24
 
0.1%
30 72
0.2%
ValueCountFrequency (%)
83 24
 
0.1%
81 168
 
0.5%
80 336
 
0.9%
79 1416
3.9%
78 2544
7.1%
77 3024
8.4%
76 2496
6.9%
75 2088
5.8%
74 1487
4.1%
73 1536
4.3%

71.4
Real number (ℝ)

Distinct447
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.734311
Minimum13.2
Maximum79.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:15.559934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum13.2
5-th percentile33.8
Q153.5
median65.9
Q373.1
95-th percentile75.7
Maximum79.8
Range66.6
Interquartile range (IQR)19.6

Descriptive statistics

Standard deviation13.680951
Coefficient of variation (CV)0.22161018
Kurtosis0.21738103
Mean61.734311
Median Absolute Deviation (MAD)8.1
Skewness-1.0219624
Sum2219410.2
Variance187.16843
MonotonicityNot monotonic
2023-02-25T06:31:15.727962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.8 624
 
1.7%
74.6 480
 
1.3%
75.1 432
 
1.2%
74.7 408
 
1.1%
73.1 360
 
1.0%
74 360
 
1.0%
75.5 336
 
0.9%
75 336
 
0.9%
73 312
 
0.9%
75.4 312
 
0.9%
Other values (437) 31991
89.0%
ValueCountFrequency (%)
13.2 24
0.1%
13.9 24
0.1%
15.2 24
0.1%
15.8 24
0.1%
16.2 24
0.1%
16.5 24
0.1%
17.8 24
0.1%
20.7 24
0.1%
21.4 24
0.1%
22 24
0.1%
ValueCountFrequency (%)
79.8 24
0.1%
79.3 24
0.1%
79.1 24
0.1%
78.3 24
0.1%
78.1 24
0.1%
77.8 24
0.1%
77.6 24
0.1%
77.4 24
0.1%
77.2 24
0.1%
77.1 48
0.1%

66
Real number (ℝ)

Distinct67
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.506412
Minimum10
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:15.913164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q145
median62
Q370
95-th percentile73
Maximum78
Range68
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.224488
Coefficient of variation (CV)0.26942939
Kurtosis-0.42382093
Mean56.506412
Median Absolute Deviation (MAD)9
Skewness-0.78618191
Sum2031462
Variance231.78504
MonotonicityNot monotonic
2023-02-25T06:31:16.081996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 2376
 
6.6%
70 2040
 
5.7%
71 2016
 
5.6%
69 1776
 
4.9%
73 1656
 
4.6%
67 1320
 
3.7%
68 1296
 
3.6%
66 1175
 
3.3%
62 1032
 
2.9%
64 936
 
2.6%
Other values (57) 20328
56.5%
ValueCountFrequency (%)
10 96
0.3%
11 24
 
0.1%
12 24
 
0.1%
13 24
 
0.1%
14 48
 
0.1%
15 24
 
0.1%
18 168
0.5%
19 144
0.4%
20 48
 
0.1%
21 96
0.3%
ValueCountFrequency (%)
78 48
 
0.1%
77 24
 
0.1%
76 96
 
0.3%
75 384
 
1.1%
74 456
 
1.3%
73 1656
4.6%
72 2376
6.6%
71 2016
5.6%
70 2040
5.7%
69 1776
4.9%

100
Real number (ℝ)

Distinct43
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.62396
Minimum50
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:16.250522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile77
Q190
median94
Q3100
95-th percentile100
Maximum100
Range50
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.7464036
Coefficient of variation (CV)0.083632826
Kurtosis4.6663033
Mean92.62396
Median Absolute Deviation (MAD)4
Skewness-1.820298
Sum3329924
Variance60.006768
MonotonicityNot monotonic
2023-02-25T06:31:16.417323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
100 9239
25.7%
93 4704
13.1%
97 4224
11.7%
90 3096
 
8.6%
94 2784
 
7.7%
96 2640
 
7.3%
87 1248
 
3.5%
85 912
 
2.5%
91 888
 
2.5%
89 768
 
2.1%
Other values (33) 5448
15.2%
ValueCountFrequency (%)
50 24
 
0.1%
52 24
 
0.1%
53 24
 
0.1%
54 72
0.2%
57 24
 
0.1%
59 48
0.1%
60 24
 
0.1%
61 96
0.3%
63 24
 
0.1%
64 72
0.2%
ValueCountFrequency (%)
100 9239
25.7%
97 4224
11.7%
96 2640
 
7.3%
94 2784
 
7.7%
93 4704
13.1%
92 48
 
0.1%
91 888
 
2.5%
90 3096
 
8.6%
89 768
 
2.1%
88 720
 
2.0%

89.4
Real number (ℝ)

Distinct468
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.487102
Minimum31.5
Maximum99.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:16.601600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum31.5
5-th percentile52.9
Q167.5
median75.2
Q382.8
95-th percentile92.6
Maximum99.9
Range68.4
Interquartile range (IQR)15.3

Descriptive statistics

Standard deviation11.806695
Coefficient of variation (CV)0.15850657
Kurtosis0.22324003
Mean74.487102
Median Absolute Deviation (MAD)7.6
Skewness-0.46246357
Sum2677885.8
Variance139.39806
MonotonicityNot monotonic
2023-02-25T06:31:16.790570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.6 312
 
0.9%
69 288
 
0.8%
80.7 264
 
0.7%
78.5 264
 
0.7%
78.8 264
 
0.7%
69.2 240
 
0.7%
72.2 240
 
0.7%
71.1 240
 
0.7%
71 240
 
0.7%
83.6 216
 
0.6%
Other values (458) 33383
92.9%
ValueCountFrequency (%)
31.5 24
0.1%
33.1 24
0.1%
34.7 24
0.1%
35.8 24
0.1%
36.1 24
0.1%
36.6 24
0.1%
40.1 24
0.1%
40.6 24
0.1%
41 24
0.1%
41.4 24
0.1%
ValueCountFrequency (%)
99.9 24
0.1%
99.6 24
0.1%
99.2 48
0.1%
99.1 24
0.1%
98.9 24
0.1%
98.4 24
0.1%
97.8 24
0.1%
97.7 24
0.1%
97.6 24
0.1%
97.5 24
0.1%

65
Real number (ℝ)

Distinct79
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.401491
Minimum15
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:16.982857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile26
Q141
median50
Q361
95-th percentile81
Maximum97
Range82
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.71328
Coefficient of variation (CV)0.30569697
Kurtosis-0.18497611
Mean51.401491
Median Absolute Deviation (MAD)10
Skewness0.31247285
Sum1847935
Variance246.90717
MonotonicityNot monotonic
2023-02-25T06:31:17.142341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44 1320
 
3.7%
47 1224
 
3.4%
49 1200
 
3.3%
52 1176
 
3.3%
54 1152
 
3.2%
53 1152
 
3.2%
46 1056
 
2.9%
48 1032
 
2.9%
55 984
 
2.7%
43 936
 
2.6%
Other values (69) 24719
68.8%
ValueCountFrequency (%)
15 72
 
0.2%
16 48
 
0.1%
17 96
0.3%
18 48
 
0.1%
19 96
0.3%
20 48
 
0.1%
21 96
0.3%
22 120
0.3%
23 216
0.6%
24 168
0.5%
ValueCountFrequency (%)
97 48
 
0.1%
96 48
 
0.1%
93 72
 
0.2%
90 168
0.5%
89 144
0.4%
88 96
 
0.3%
87 288
0.8%
86 120
0.3%
85 72
 
0.2%
84 240
0.7%

21
Real number (ℝ)

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.899725
Minimum6
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:17.287409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9
Q113
median15
Q318
95-th percentile25
Maximum39
Range33
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.8515931
Coefficient of variation (CV)0.30513693
Kurtosis1.1858103
Mean15.899725
Median Absolute Deviation (MAD)3
Skewness0.77470198
Sum571611
Variance23.537956
MonotonicityNot monotonic
2023-02-25T06:31:17.426670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
15 3840
10.7%
14 3816
10.6%
13 3120
8.7%
16 3096
8.6%
12 2952
8.2%
17 2856
 
7.9%
18 2640
 
7.3%
10 2448
 
6.8%
20 2088
 
5.8%
21 1751
 
4.9%
Other values (16) 7344
20.4%
ValueCountFrequency (%)
6 144
 
0.4%
7 432
 
1.2%
8 840
 
2.3%
9 1368
 
3.8%
10 2448
6.8%
12 2952
8.2%
13 3120
8.7%
14 3816
10.6%
15 3840
10.7%
16 3096
8.6%
ValueCountFrequency (%)
39 24
 
0.1%
36 120
 
0.3%
33 96
 
0.3%
31 72
 
0.2%
30 72
 
0.2%
29 144
 
0.4%
28 336
0.9%
26 408
1.1%
25 528
1.5%
24 528
1.5%

9.5
Real number (ℝ)

Distinct151
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7689772
Minimum1.6
Maximum23.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:17.621766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile3.6
Q15.7
median7.3
Q39.5
95-th percentile13.5
Maximum23.9
Range22.3
Interquartile range (IQR)3.8

Descriptive statistics

Standard deviation3.0069758
Coefficient of variation (CV)0.38704912
Kurtosis1.3078829
Mean7.7689772
Median Absolute Deviation (MAD)1.9
Skewness0.88405544
Sum279302.5
Variance9.0419035
MonotonicityNot monotonic
2023-02-25T06:31:17.816381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3 720
 
2.0%
7 672
 
1.9%
6 672
 
1.9%
7.2 672
 
1.9%
7.3 648
 
1.8%
7.8 624
 
1.7%
5.8 624
 
1.7%
6.7 600
 
1.7%
6.8 576
 
1.6%
7.7 552
 
1.5%
Other values (141) 29591
82.3%
ValueCountFrequency (%)
1.6 48
 
0.1%
1.9 24
 
0.1%
2 24
 
0.1%
2.2 72
0.2%
2.3 48
 
0.1%
2.4 96
0.3%
2.5 24
 
0.1%
2.6 72
0.2%
2.7 144
0.4%
2.8 96
0.3%
ValueCountFrequency (%)
23.9 24
0.1%
21.9 24
0.1%
20.6 24
0.1%
20 24
0.1%
18.6 48
0.1%
18.4 24
0.1%
18.1 24
0.1%
17.7 24
0.1%
17.5 48
0.1%
17.2 24
0.1%

0.2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3912269
Minimum0
Maximum17
Zeros25943
Zeros (%)72.2%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:17.967237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile7
Maximum17
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5152685
Coefficient of variation (CV)1.8079498
Kurtosis3.3027344
Mean1.3912269
Median Absolute Deviation (MAD)0
Skewness1.8494483
Sum50016
Variance6.3265756
MonotonicityNot monotonic
2023-02-25T06:31:18.103689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 25943
72.2%
3 4032
 
11.2%
5 2496
 
6.9%
6 1656
 
4.6%
7 624
 
1.7%
8 624
 
1.7%
9 264
 
0.7%
10 120
 
0.3%
12 72
 
0.2%
14 48
 
0.1%
Other values (2) 72
 
0.2%
ValueCountFrequency (%)
0 25943
72.2%
3 4032
 
11.2%
5 2496
 
6.9%
6 1656
 
4.6%
7 624
 
1.7%
8 624
 
1.7%
9 264
 
0.7%
10 120
 
0.3%
12 72
 
0.2%
13 48
 
0.1%
ValueCountFrequency (%)
17 24
 
0.1%
14 48
 
0.1%
13 48
 
0.1%
12 72
 
0.2%
10 120
 
0.3%
9 264
 
0.7%
8 624
 
1.7%
7 624
 
1.7%
6 1656
4.6%
5 2496
6.9%

29.8
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.977308
Minimum29.5
Maximum30.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:18.267817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum29.5
5-th percentile29.7
Q129.9
median29.9
Q330.1
95-th percentile30.3
Maximum30.6
Range1.1
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.16730273
Coefficient of variation (CV)0.0055809793
Kurtosis0.80144177
Mean29.977308
Median Absolute Deviation (MAD)0.1
Skewness0.73652364
Sum1077714.2
Variance0.027990205
MonotonicityNot monotonic
2023-02-25T06:31:18.398965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
29.9 11952
33.2%
30 6984
19.4%
29.8 5015
13.9%
30.1 4968
13.8%
30.2 2640
 
7.3%
30.3 1464
 
4.1%
29.7 1416
 
3.9%
30.4 792
 
2.2%
29.6 360
 
1.0%
30.5 192
 
0.5%
Other values (2) 168
 
0.5%
ValueCountFrequency (%)
29.5 24
 
0.1%
29.6 360
 
1.0%
29.7 1416
 
3.9%
29.8 5015
13.9%
29.9 11952
33.2%
30 6984
19.4%
30.1 4968
13.8%
30.2 2640
 
7.3%
30.3 1464
 
4.1%
30.4 792
 
2.2%
ValueCountFrequency (%)
30.6 144
 
0.4%
30.5 192
 
0.5%
30.4 792
 
2.2%
30.3 1464
 
4.1%
30.2 2640
 
7.3%
30.1 4968
13.8%
30 6984
19.4%
29.9 11952
33.2%
29.8 5015
13.9%
29.7 1416
 
3.9%

29.8.1
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.906144
Minimum28.8
Maximum30.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:18.553228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum28.8
5-th percentile29.7
Q129.8
median29.9
Q330
95-th percentile30.2
Maximum30.6
Range1.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1619829
Coefficient of variation (CV)0.0054163754
Kurtosis2.2804166
Mean29.906144
Median Absolute Deviation (MAD)0.1
Skewness0.36242246
Sum1075155.8
Variance0.026238461
MonotonicityNot monotonic
2023-02-25T06:31:18.700647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
29.9 11496
32.0%
29.8 8807
24.5%
30 5280
14.7%
30.1 3360
 
9.3%
29.7 2952
 
8.2%
30.2 1416
 
3.9%
29.6 1104
 
3.1%
30.3 864
 
2.4%
29.5 264
 
0.7%
30.4 216
 
0.6%
Other values (4) 192
 
0.5%
ValueCountFrequency (%)
28.8 24
 
0.1%
29.4 24
 
0.1%
29.5 264
 
0.7%
29.6 1104
 
3.1%
29.7 2952
 
8.2%
29.8 8807
24.5%
29.9 11496
32.0%
30 5280
14.7%
30.1 3360
 
9.3%
30.2 1416
 
3.9%
ValueCountFrequency (%)
30.6 24
 
0.1%
30.5 120
 
0.3%
30.4 216
 
0.6%
30.3 864
 
2.4%
30.2 1416
 
3.9%
30.1 3360
 
9.3%
30 5280
14.7%
29.9 11496
32.0%
29.8 8807
24.5%
29.7 2952
 
8.2%

29.7
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.814756
Minimum0
Maximum30.5
Zeros24
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:18.856644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.6
Q129.7
median29.8
Q329.9
95-th percentile30.1
Maximum30.5
Range30.5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.78699163
Coefficient of variation (CV)0.026396044
Kurtosis1372.4081
Mean29.814756
Median Absolute Deviation (MAD)0.1
Skewness-36.294507
Sum1071870.3
Variance0.61935582
MonotonicityNot monotonic
2023-02-25T06:31:18.989580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
29.8 11400
31.7%
29.9 8040
22.4%
29.7 5735
16.0%
30 3312
 
9.2%
29.6 2904
 
8.1%
30.1 2328
 
6.5%
30.2 792
 
2.2%
29.5 768
 
2.1%
30.3 240
 
0.7%
29.4 192
 
0.5%
Other values (4) 240
 
0.7%
ValueCountFrequency (%)
0 24
 
0.1%
29.3 24
 
0.1%
29.4 192
 
0.5%
29.5 768
 
2.1%
29.6 2904
 
8.1%
29.7 5735
16.0%
29.8 11400
31.7%
29.9 8040
22.4%
30 3312
 
9.2%
30.1 2328
 
6.5%
ValueCountFrequency (%)
30.5 24
 
0.1%
30.4 168
 
0.5%
30.3 240
 
0.7%
30.2 792
 
2.2%
30.1 2328
 
6.5%
30 3312
 
9.2%
29.9 8040
22.4%
29.8 11400
31.7%
29.7 5735
16.0%
29.6 2904
 
8.1%

0.3
Real number (ℝ)

Distinct143
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16007121
Minimum0
Maximum13.43
Zeros24983
Zeros (%)69.5%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:19.168713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02
95-th percentile0.93
Maximum13.43
Range13.43
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.66791533
Coefficient of variation (CV)4.1726138
Kurtosis176.1312
Mean0.16007121
Median Absolute Deviation (MAD)0
Skewness11.312773
Sum5754.72
Variance0.44611088
MonotonicityNot monotonic
2023-02-25T06:31:19.368749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24983
69.5%
0.01 1344
 
3.7%
0.02 840
 
2.3%
0.03 480
 
1.3%
0.04 408
 
1.1%
0.07 312
 
0.9%
0.05 312
 
0.9%
0.09 240
 
0.7%
0.19 240
 
0.7%
0.17 216
 
0.6%
Other values (133) 6576
 
18.3%
ValueCountFrequency (%)
0 24983
69.5%
0.01 1344
 
3.7%
0.02 840
 
2.3%
0.03 480
 
1.3%
0.04 408
 
1.1%
0.05 312
 
0.9%
0.06 168
 
0.5%
0.07 312
 
0.9%
0.08 120
 
0.3%
0.09 240
 
0.7%
ValueCountFrequency (%)
13.43 24
0.1%
10.75 24
0.1%
9.21 24
0.1%
5.71 24
0.1%
5.28 24
0.1%
4.61 24
0.1%
4.33 24
0.1%
4.15 24
0.1%
3.18 24
0.1%
3.15 24
0.1%

1.057
Real number (ℝ)

Distinct4172
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89162657
Minimum0.064
Maximum6.446
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.0 KiB
2023-02-25T06:31:19.589702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.064
5-th percentile0.237
Q10.3235
median0.501
Q31.072
95-th percentile2.9805
Maximum6.446
Range6.382
Interquartile range (IQR)0.7485

Descriptive statistics

Standard deviation0.90814476
Coefficient of variation (CV)1.0185259
Kurtosis4.5331241
Mean0.89162657
Median Absolute Deviation (MAD)0.22
Skewness2.1284914
Sum32054.867
Variance0.8247269
MonotonicityNot monotonic
2023-02-25T06:31:19.779491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.278 109
 
0.3%
0.288 108
 
0.3%
0.292 105
 
0.3%
0.3 104
 
0.3%
0.307 102
 
0.3%
0.29 102
 
0.3%
0.281 101
 
0.3%
0.283 101
 
0.3%
0.28 100
 
0.3%
0.329 99
 
0.3%
Other values (4162) 34920
97.1%
ValueCountFrequency (%)
0.064 1
 
< 0.1%
0.098 1
 
< 0.1%
0.112 1
 
< 0.1%
0.131 1
 
< 0.1%
0.134 2
< 0.1%
0.135 1
 
< 0.1%
0.136 1
 
< 0.1%
0.139 1
 
< 0.1%
0.14 3
< 0.1%
0.141 3
< 0.1%
ValueCountFrequency (%)
6.446 1
< 0.1%
6.142 1
< 0.1%
5.607 1
< 0.1%
5.488 1
< 0.1%
5.485 1
< 0.1%
5.47 1
< 0.1%
5.43 1
< 0.1%
5.426 1
< 0.1%
5.416 1
< 0.1%
5.37 1
< 0.1%

Interactions

2023-02-25T06:31:07.766504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:29:58.499746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:02.035590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:05.054121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:08.486684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:11.864024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:15.710578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:19.372130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:23.355479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:26.685044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:30.072066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:33.678600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:36.780857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:40.050989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:43.361432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:47.276498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:50.419116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:54.179948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:57.411889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:00.852107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:04.588063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:07.912735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:29:58.695882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:02.183087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:05.391235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:08.633316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:12.034332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:15.892280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:19.605645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:23.520458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:26.922116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:30.224034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:33.826146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:36.941705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:40.198676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:43.519820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:47.425924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:50.567606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:54.326695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:57.564011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:01.004179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:04.738337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:08.050192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:29:58.827568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:02.306591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:05.587976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:08.766977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:12.422263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:16.083619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:19.790119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:23.670493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:27.077347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:30.368315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:33.969315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:37.089746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:40.334823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:43.665066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:47.607319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:50.713814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:54.472696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:57.705277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:01.140837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:04.873245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:08.196788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:29:58.989720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:02.457155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:05.719674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:08.921900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:12.626743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:16.251278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:19.956303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:23.830657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:27.223666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:30.525988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:34.113246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:37.243568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:40.478463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:43.816498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:47.749407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:50.860862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:54.622905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:57.854700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:01.299731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:05.031291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:08.334192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:29:59.144842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:02.602576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:05.870518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:09.056247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:12.783949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:16.413484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:20.107747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:23.986037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:27.357660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:30.679803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:34.251312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:37.391324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:40.617572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:43.968568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:47.884671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:51.005991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:54.773839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:58.000138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:01.446495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:05.171132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:08.481815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:29:59.292806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:02.738614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:06.021298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:09.221180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:12.997841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:16.591501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:20.250411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:24.131091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:27.505627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:30.838890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:34.394723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:37.540757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:40.764522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:44.161905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:48.046886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:51.165119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:54.949217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:58.154290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:01.595565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:05.325942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:08.628466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:29:59.452095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:02.889579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:06.169682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:09.366812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:13.146111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:16.774546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:20.419831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:24.285894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:27.653846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:30.994297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:34.540677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:37.684115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:40.920552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:44.314847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:48.193347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:51.314344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:55.104254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:58.328842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:01.748728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:05.478223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:08.782096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:29:59.758022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:03.037179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:06.303932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:09.516219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:13.298044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:16.965068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:20.567854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:24.433655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:27.805263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:31.154656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:34.686410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:37.830711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:41.146754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:44.475332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:48.333559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:51.473666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:55.264369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:58.502128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:01.905355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:05.637329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:09.013047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:29:59.930429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:03.172638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:06.453463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:09.684534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:13.460987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:17.142113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:20.724158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:24.582534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:27.973304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:31.306428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:34.832289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:37.980697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:41.338723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:44.634413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:48.484258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:51.616133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:55.415246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:58.661949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:02.062484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:05.786592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:09.178702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:00.088099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:03.323498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:06.592417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:09.920517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:13.618426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:17.307901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:20.893024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:24.734706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:28.136413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:31.467511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:34.980561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:38.134664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:41.529244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:44.789873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:48.637809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:51.767520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:55.575346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:58.824339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:02.214168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:05.941451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:09.334236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:00.233191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:03.473452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:06.749640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:10.084479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:13.776807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:17.486672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:21.080723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:24.890339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:28.296873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:31.625846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:35.155831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:38.295293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:41.675821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:44.946384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:48.790470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:51.924753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:55.730816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:58.989417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:02.370470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:06.099983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:09.483040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:00.411264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:03.639273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:06.892579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:10.233546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:13.918300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:17.658223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:21.237327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:25.048190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:28.506843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:31.772385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:35.291857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:38.450777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:41.814098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:45.091180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:48.934532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:52.080985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:55.878790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:59.211705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:02.513121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:06.239935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:09.633403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:00.566550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:03.807499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:07.048695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:10.399461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:14.068409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:17.818162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:21.420293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:25.261839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:28.666109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:31.936147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:35.455558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:38.603004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:41.984783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:45.357100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:49.102220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:52.245236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:56.042197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:59.388174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:03.243878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:06.396131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:09.771683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:00.708796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:03.937418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:07.207441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:10.540370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:14.214192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:18.019375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:21.637624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:25.412511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:28.807000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:32.080252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:35.606458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:38.744360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:42.125135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:45.517143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:49.238304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:52.467245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:56.185864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:59.560923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:03.380883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:06.535549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:09.926398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:00.875646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:04.075042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:07.370826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:10.707588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:14.402256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:18.206542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:22.139393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:25.565417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:28.972282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:32.239334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:35.752488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:38.906809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:42.275809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:45.677683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:49.394970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:52.663429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:56.347649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:59.725915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:03.537782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:06.690020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:10.067950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:01.075972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:04.210697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:07.519551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:10.853947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:14.560915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:18.345689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:22.314499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:25.703305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:29.124852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:32.383123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:35.881803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:39.079374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:42.415473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:46.273228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:49.528868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:52.852021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:56.491161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:59.868941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:03.675001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:06.827265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:10.222331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:01.244413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:04.355412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:07.706755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:11.003678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:14.728571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:18.511593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:22.536340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:25.854081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:29.281718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:32.884685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:36.030930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:39.255515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:42.584918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:46.447337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:49.682265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:53.201722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:56.647137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:00.048675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:03.826100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:06.985652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:10.371077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:01.389941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:04.490314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:07.866344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:11.155437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:14.884254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:18.659622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:22.707614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:26.013184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:29.437091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:33.055170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:36.171168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:39.423128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:42.737784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:46.607062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:49.826483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:53.380905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:56.796922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:00.197787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:03.979182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:07.145118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:10.524890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:01.558461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:04.637644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:08.025624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:11.317573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:15.062647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:18.828094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:22.874951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:26.168711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:29.602009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:33.215079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:36.335822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:39.592699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:42.902345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:46.769686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:49.980911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:53.572918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:56.962023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:00.360677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:04.140021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:07.310156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:10.674443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:01.734164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:04.771564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:08.169688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:11.475432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:15.217767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:18.998472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:23.029592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:26.313951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:29.759454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:33.370755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:36.482584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:39.748430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:43.057241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:46.929939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:50.126685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:53.749086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:57.118151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:00.525278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:04.282574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:07.455390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:10.821732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:01.887762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:04.921251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:08.317353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:11.653230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:15.410686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:19.151547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:23.194612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:26.460572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:29.914198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:33.526628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:36.631287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:39.905223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:43.209012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:47.117970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:50.275988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:54.011006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:30:57.270590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:00.693757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:04.436411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-25T06:31:07.611904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-25T06:31:20.732219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
20618574.8687471.46610089.465219.50.229.829.8.129.70.31.0572.11.12016
21.000-0.000-0.0050.0090.0190.0170.0170.0160.0180.0100.0170.0030.0000.0090.016-0.023-0.001-0.019-0.0090.0060.0230.5480.0000.000
0-0.0001.000-0.000-0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000-0.000-0.000-0.000-0.000-0.0000.2500.0000.0000.000
6-0.005-0.0001.0000.0120.0370.0290.0390.0450.0420.0470.0100.003-0.0210.007-0.026-0.029-0.057-0.031-0.0260.008-0.0090.1150.0090.129
10.009-0.0000.0121.0000.0430.0390.0370.0610.0560.0440.0220.0280.018-0.087-0.086-0.0090.0300.0330.036-0.0160.0320.2521.0000.144
850.0190.0000.0370.0431.0000.9730.9050.8460.8500.8130.021-0.119-0.132-0.043-0.244-0.184-0.560-0.464-0.387-0.1170.4320.1110.3140.146
74.80.0170.0000.0290.0390.9731.0000.9660.9000.9130.8800.029-0.038-0.0040.003-0.159-0.107-0.592-0.493-0.415-0.0820.4380.1050.3100.128
680.0170.0000.0390.0370.9050.9661.0000.9120.9410.9250.0520.0820.1270.041-0.094-0.031-0.600-0.492-0.413-0.0160.4230.1070.3020.128
740.0160.0000.0450.0610.8460.9000.9121.0000.9710.9160.2790.2800.2700.096-0.102-0.097-0.588-0.514-0.4480.0820.4120.1100.2910.148
71.40.0180.0000.0420.0560.8500.9130.9410.9711.0000.9720.2710.3090.3120.060-0.122-0.086-0.601-0.506-0.4230.0590.4130.1120.2820.149
660.0100.0000.0470.0440.8130.8800.9250.9160.9721.0000.2630.3180.3390.027-0.141-0.086-0.595-0.478-0.3870.0680.3990.1030.2810.147
1000.0170.0000.0100.0220.0210.0290.0520.2790.2710.2631.0000.7170.494-0.014-0.152-0.202-0.149-0.159-0.1480.2640.0250.1290.1050.176
89.40.0030.0000.0030.028-0.119-0.0380.0820.2800.3090.3180.7171.0000.8980.1390.0970.053-0.198-0.210-0.1990.347-0.0150.0810.1230.132
650.0000.000-0.0210.018-0.132-0.0040.1270.2700.3120.3390.4940.8981.0000.1940.2410.196-0.191-0.195-0.1810.286-0.0060.0780.1670.101
210.0090.0000.007-0.087-0.0430.0030.0410.0960.0600.027-0.0140.1390.1941.0000.7380.275-0.134-0.188-0.2390.115-0.0030.0770.1140.084
9.50.0160.000-0.026-0.086-0.244-0.159-0.094-0.102-0.122-0.141-0.1520.0970.2410.7381.0000.613-0.040-0.100-0.1660.079-0.1020.0830.1440.126
0.2-0.023-0.000-0.029-0.009-0.184-0.107-0.031-0.097-0.086-0.086-0.2020.0530.1960.2750.6131.0000.014-0.014-0.0540.008-0.0730.0670.0850.095
29.8-0.001-0.000-0.0570.030-0.560-0.592-0.600-0.588-0.601-0.595-0.149-0.198-0.191-0.134-0.0400.0141.0000.8990.860-0.104-0.2360.0990.2180.102
29.8.1-0.019-0.000-0.0310.033-0.464-0.493-0.492-0.514-0.506-0.478-0.159-0.210-0.195-0.188-0.100-0.0140.8991.0000.915-0.114-0.1820.0890.1740.095
29.7-0.009-0.000-0.0260.036-0.387-0.415-0.413-0.448-0.423-0.387-0.148-0.199-0.181-0.239-0.166-0.0540.8600.9151.000-0.142-0.1460.0170.0430.062
0.30.006-0.0000.008-0.016-0.117-0.082-0.0160.0820.0590.0680.2640.3470.2860.1150.0790.008-0.104-0.114-0.1421.000-0.0330.0510.0650.055
1.0570.0230.250-0.0090.0320.4320.4380.4230.4120.4130.3990.025-0.015-0.006-0.003-0.102-0.073-0.236-0.182-0.146-0.0331.0000.0570.1650.103
2.10.5480.0000.1150.2520.1110.1050.1070.1100.1120.1030.1290.0810.0780.0770.0830.0670.0990.0890.0170.0510.0571.0000.2040.402
1.10.0000.0000.0091.0000.3140.3100.3020.2910.2820.2810.1050.1230.1670.1140.1440.0850.2180.1740.0430.0650.1650.2041.0000.159
20160.0000.0000.1290.1440.1460.1280.1280.1480.1490.1470.1760.1320.1010.0840.1260.0950.1020.0950.0620.0550.1030.4020.1591.000

Missing values

2023-02-25T06:31:11.079417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-25T06:31:11.574791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

22.100.1611.120168574.8687471.46610089.465219.50.229.829.8.129.70.31.057
0221061120168574.8687471.46610089.465219.5029.829.829.70.01.171
1222061120168574.8687471.46610089.465219.5029.829.829.70.00.560
2223061120168574.8687471.46610089.465219.5029.829.829.70.00.828
3224061120168574.8687471.46610089.465219.5029.829.829.70.00.932
4225061120168574.8687471.46610089.465219.5029.829.829.70.00.333
5226061120168574.8687471.46610089.465219.5029.829.829.70.00.462
6227061120168574.8687471.46610089.465219.5029.829.829.70.00.493
7228061120168574.8687471.46610089.465219.5029.829.829.70.00.325
8229061120168574.8687471.46610089.465219.5029.829.829.70.00.294
92210061120168574.8687471.46610089.465219.5029.829.829.70.00.273
22.100.1611.120168574.8687471.46610089.465219.50.229.829.8.129.70.31.057
359411214077320209384.5808076.0729176.653156.1029.929.829.80.03.212
359421215077320209384.5808076.0729176.653156.1029.929.829.80.02.985
359431216077320209384.5808076.0729176.653156.1029.929.829.80.02.995
359441217077320209384.5808076.0729176.653156.1029.929.829.80.02.930
359451218077320209384.5808076.0729176.653156.1029.929.829.80.01.296
359461219077320209384.5808076.0729176.653156.1029.929.829.80.01.307
359471220077320209384.5808076.0729176.653156.1029.929.829.80.02.872
359481221077320209384.5808076.0729176.653156.1029.929.829.80.02.138
359491222077320209384.5808076.0729176.653156.1029.929.829.80.02.199
359501223077320209384.5808076.0729176.653156.1029.929.829.80.01.809